class_docstr: Extension of Transresnet to incorporate dialogue history and multimodality.
docstr: Choose the best response for each example.
  
  :param image_features:
      list of tensors of image features
  :param personalities:
      list of personalities
  :param dialogue_histories:
      list of dialogue histories, one per example
  :param candidates:
      list of candidates, one set per example
  :param candidates_encoded:
      optional; if specified, a fixed set of encoded candidates that is
      used for each example
  :param k:
      number of ranked candidates to return. if < 1, we return the ranks
      of all candidates in the set.
  
  :return:
      a set of ranked candidates for each example
docstr_tok: ["Choose","the","best","response","for","each","example",".",":","param","image_features",":","list","of","tensors","of","image","features",":","param","personalities",":","list","of","personalities",":","param","dialogue_histories",":","list","of","dialogue","histories",",","one","per","example",":","param","candidates",":","list","of","candidates",",","one","set","per","example",":","param","candidates_encoded",":","optional",";","if","specified",",","a","fixed","set","of","encoded","candidates","that","is","used","for","each","example",":","param","k",":","number","of","ranked","candidates","to","return",".","if","<","1",",","we","return","the","ranks","of","all","candidates","in","the","set",".",":","return",":","a","set","of","ranked","candidates","for","each","example"]
code: def choose_best_response(
      self,
      image_features,
      personalities,
      dialogue_histories,
      candidates,
      candidates_encoded=None,
      k=1,
      batchsize=None,
  ):
      """
      Choose the best response for each example.
  
      :param image_features:
          list of tensors of image features
      :param personalities:
          list of personalities
      :param dialogue_histories:
          list of dialogue histories, one per example
      :param candidates:
          list of candidates, one set per example
      :param candidates_encoded:
          optional; if specified, a fixed set of encoded candidates that is
          used for each example
      :param k:
          number of ranked candidates to return. if < 1, we return the ranks
          of all candidates in the set.
  
      :return:
          a set of ranked candidates for each example
      """
      self.eval()
      _, _, encoded = self.forward(
          image_features,
          personalities,
          dialogue_histories,
          None,
          batchsize=batchsize,
      )
      encoded = encoded.detach()
      one_cand_set = True
      if candidates_encoded is None:
          one_cand_set = False
          candidates_encoded = [
              self.forward_text_encoder(c).detach() for c in candidates
          ]
      chosen = [
          self.choose_topk(
              idx if not one_cand_set else 0,
              encoded,
              candidates,
              candidates_encoded,
              one_cand_set,
              k,
          )
          for idx in range(len(encoded))
      ]
      return chosen
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from parlai.utils.io import PathManager
  from parlai.agents.transformer.modules import TransformerEncoder, create_position_codes, TransformerEncoderLayer
  from projects.personality_captions.transresnet.modules import TransresnetModel, load_fasttext_embeddings
label_desc: Use image node_id 10 for calling the TransresnetMultimodalModel obj's underlying member method code with example usage: obj.choose_best_response(image_features, personalities, dialogue_histories, candidates, candidates_encoded, k, batchsize) and returns: chosen